[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31266":3,"doc-seo-31266":26},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"table_of_contents":22,"faqs":23,"seo_title":13,"seo_description":14,"update_tm":24,"read_time":25},31266,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Privacy Issues in Large Language Models: The Digital Study of Ollama","The study investigates privacy risks in locally executed large language models by examining volatile memory traces produced when querying DeepSeek-R1 through the Ollama framework. RAM dumps are recorded after model interaction and analyzed with Volatility 3 to detect GPU memory artifacts, network connections, process hierarchies, prompt tokens resident in memory, and other security-relevant identifiers. Findings show model-related strings, active network sessions, and plaintext query tokens may persist in RAM, enabling evidence-oriented privacy risk assessment. A reproducible forensic workflow is provided and compared with related memory-forensics research.","cbCaiq98FJc60Atc","https://ap.wps.com/l/cbCaiq98FJc60Atc","pdf",275719,1,5,"English","# Introduction\n# Literature Review\n# Research Gap and Contribution\n# Paper Organization","[{\"question\":\"What privacy risk does the paper focus on for locally run LLMs?\",\"answer\":\"It focuses on privacy leakage that can occur through volatile memory, where forensic traces may remain in RAM after the model runs locally.\"},{\"question\":\"How were RAM traces collected in the study?\",\"answer\":\"FTK Imager was used to record a RAM dump after querying the DeepSeek-R1 model via the Ollama interface.\"},{\"question\":\"Which types of sensitive data or artifacts did the analysis find in RAM?\",\"answer\":\"The analysis found model-related strings, active network sessions, and plaintext query tokens that can remain in RAM, which are relevant for forensic investigations and privacy assessments.\"}]",1779224606,13,{"code":4,"msg":27,"data":28},"ok",{"site_id":29,"language":30,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":24},105,"en","privacy-issues-in-large-language-models-the-digital-study-of-ollama","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/privacy-issues-in-large-language-models-the-digital-study-of-ollama/31266/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-19",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What privacy risk does the paper focus on for locally run LLMs?","Question",{"text":73,"@type":74},"It focuses on privacy leakage that can occur through volatile memory, where forensic traces may remain in RAM after the model runs locally.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How were RAM traces collected in the study?",{"text":78,"@type":74},"FTK Imager was used to record a RAM dump after querying the DeepSeek-R1 model via the Ollama interface.",{"name":80,"@type":71,"acceptedAnswer":81},"Which types of sensitive data or artifacts did the analysis find in RAM?",{"text":82,"@type":74},"The analysis found model-related strings, active network sessions, and plaintext query tokens that can remain in RAM, which are relevant for forensic investigations and privacy assessments.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":29}]